#look into methods for estimating pi0 conservatively

Do some simulations

opts_knit$set(progress = TRUE, verbose = TRUE, root.dir = "~/Documents/git/ash/paper/Rcode")
require(ashr)
## Loading required package: ashr
## Loading required package: truncnorm
require(qvalue)
## Loading required package: qvalue
require(fdrtool)
## Loading required package: fdrtool
require(mixfdr)
## Loading required package: mixfdr
require(locfdr)
## Loading required package: locfdr
## Loading required package: splines
require(ggplot2)
## Loading required package: ggplot2
## 
## Attaching package: 'ggplot2'
## 
## The following object is masked from 'package:qvalue':
## 
##     qplot
# bsd gives standard deviation of beta pi0 is simulated to be uniform on
# [minpi0,1]
basicsim = function(mixsd, mixpi_alt, bsd = 1, minpi0 = 0, seedval = 100, nsamp = 1000, 
    niter = 50) {
    set.seed(seedval)
    beta = list()
    betahatsd = list()
    betahat = list()
    zscore = list()
    pval = list()
    betahat.ash.n = list()
    betahat.ash.u = list()
    betahat.ash.npm = list()
    betahat.ash.true = list()
    betahat.qval = list()
    betahat.fdrtool = list()
    betahat.locfdr = list()
    betahat.mixfdr = list()
    pi0 = rep(0, niter)
    for (i in 1:niter) {
        pi0[i] = runif(1, minpi0, 1)
        mixpi = c(pi0[i], (1 - pi0[i]) * mixpi_alt)
        sd = sample(mixsd, nsamp, prob = mixpi, replace = TRUE)
        beta[[i]] = rnorm(nsamp, 0, sd)
        betahatsd[[i]] = bsd
        betahat[[i]] = beta[[i]] + rnorm(nsamp, 0, betahatsd[[i]])
        zscore[[i]] = betahat[[i]]/betahatsd[[i]]
        pval[[i]] = pchisq(zscore[[i]]^2, df = 1, lower.tail = F)
        betahat.ash.n[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = TRUE, 
            prior = "nullbiased", gridmult = 2)
        betahat.ash.u[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = TRUE, 
            prior = "uniform", gridmult = 2)
        betahat.ash.npm[[i]] = ash(betahat[[i]], betahatsd[[i]], pointmass = FALSE, 
            prior = "uniform", gridmult = 2)
        betahat.ash.true[[i]] = ash(betahat[[i]], betahatsd[[i]], g = normalmix(mixpi, 
            rep(0, length(mixpi)), mixsd))

        betahat.qval[[i]] = qvalue(pval[[i]])
        betahat.fdrtool[[i]] = fdrtool(pval[[i]], statistic = "pvalue", plot = FALSE)
        betahat.locfdr[[i]] = locfdr(zscore[[i]], nulltype = 0, plot = 0)
        betahat.mixfdr[[i]] = mixFdr(zscore[[i]], noiseSD = 1, theonull = TRUE, 
            plot = FALSE)
    }
    return(list(beta = beta, betahatsd = betahatsd, betahat = betahat, zscore = zscore, 
        pval = pval, betahat.ash.n = betahat.ash.n, betahat.ash.u = betahat.ash.u, 
        betahat.ash.npm = betahat.ash.npm, betahat.ash.true = betahat.ash.true, 
        betahat.qval = betahat.qval, betahat.fdrtool = betahat.fdrtool, betahat.locfdr = betahat.locfdr, 
        betahat.mixfdr = betahat.mixfdr, pi0 = pi0))
}
mixsd = c(0, 0.25, 0.5, 1, 2)
mixpi_alt = c(0.4, 0.2, 0.2, 0.2)  #mixture proportions under the alternative

simres1 = basicsim(mixsd, mixpi_alt, niter = 200, nsamp = 1000)
## Loading required package: fdrtool
## Loading required package: mixfdr
## Loading required package: locfdr
## Loading required package: splines
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9415  0.0215  0.0371  
## 
## mu =          0.000  -2.903   2.983  
## 
## sigma =  1.000   1.348   1.093   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9538  0.0200  0.0262  
## 
## mu =          0.000   3.065  -3.279  
## 
## sigma =  1.000   1.507   1.199   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9557  0.0325  0.0117  
## 
## mu =          0.000  -2.394   3.255  
## 
## sigma =  1.000   1.000   1.481   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9611  0.0175  0.0215  
## 
## mu =          0.000  -3.053   3.404  
## 
## sigma =  1.000   1.255   1.720   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8991  0.0529  0.0481  
## 
## mu =          0.000   2.724  -2.924  
## 
## sigma =  1.000   1.124   1.091   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9790  0.0113  0.0097  
## 
## mu =          0.000   2.630  -2.916  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9555  0.0222  0.0223  
## 
## mu =          0.000   3.141  -2.879  
## 
## sigma =  1.000   1.000   1.085   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9577  0.0203  0.0220  
## 
## mu =          0.000  -3.017   3.480  
## 
## sigma =  1.000   1.590   1.337   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9839  0.0085  0.0076  
## 
## mu =          0.000   2.900  -3.417  
## 
## sigma =  1.000   1.000   1.001   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9724  0.0155  0.0121  
## 
## mu =          0.000  -3.059   2.982  
## 
## sigma =  1.000   1.159   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9388  0.0323  0.0289  
## 
## mu =          0.000  -2.754   3.300  
## 
## sigma =  1.000   1.193   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9260  0.0349  0.0391  
## 
## mu =          0.000  -3.115   2.802  
## 
## sigma =  1.000   1.182   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9532  0.0130  0.0338  
## 
## mu =          0.000  -3.668   3.145  
## 
## sigma =  1.000   1.597   1.302   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9714  0.0109  0.0177  
## 
## mu =          0.000  -2.721   2.741  
## 
## sigma =  1.000   1.503   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9969  0.0019  0.0012  
## 
## mu =          0.000   2.942  -1.582  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9782  0.0071  0.0147  
## 
## mu =          0.000   3.043  -2.927  
## 
## sigma =  1.000   1.063   1.026   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9939  0.0051  0.0010  
## 
## mu =          0.000  -2.661   4.777  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9454  0.0263  0.0283  
## 
## mu =          0.000  -3.120   2.732  
## 
## sigma =  1.000   1.141   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9702  0.0142  0.0155  
## 
## mu =          0.000   2.934  -3.007  
## 
## sigma =  1.000   1.722   1.275   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9529  0.0269  0.0202  
## 
## mu =          0.000   2.978  -3.445  
## 
## sigma =  1.000   1.287   1.123   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9195  0.0441  0.0364  
## 
## mu =          0.000   2.816  -3.097  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9351  0.0417  0.0232  
## 
## mu =          0.000   2.636  -3.357  
## 
## sigma =  1.000   1.000   1.355   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9622  0.0192  0.0186  
## 
## mu =          0.000  -3.162   2.876  
## 
## sigma =  1.000   1.246   1.081   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9397  0.0411  0.0192  
## 
## mu =          0.000   2.769  -2.945  
## 
## sigma =  1.000   1.002   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9732  0.0134  0.0134  
## 
## mu =          0.000   3.312  -2.945  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9708  0.0204  0.0088  
## 
## mu =          0.000  -2.822   3.111  
## 
## sigma =  1.000   1.255   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9711  0.0200  0.0090  
## 
## mu =          0.000   3.288  -3.149  
## 
## sigma =  1.000   1.512   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9811  0.0061  0.0128  
## 
## mu =          0.000   3.024  -3.304  
## 
## sigma =  1.000   1.000   1.194   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9848  0.0074  0.0078  
## 
## mu =          0.000  -3.092   2.293  
## 
## sigma =  1.000   1.196   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9807  0.0095  0.0098  
## 
## mu =          0.000  -3.299   2.603  
## 
## sigma =  1.000   1.143   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9479  0.0248  0.0274  
## 
## mu =          0.000  -2.994   3.158  
## 
## sigma =  1.000   1.272   1.593   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9808  0.0104  0.0088  
## 
## mu =          0.000  -2.661   3.815  
## 
## sigma =  1.000   1.064   1.483   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9832  0.0121  0.0047  
## 
## mu =          0.000   3.765  -4.055  
## 
## sigma =  1.000   1.000   1.478   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9792  0.0082  0.0126  
## 
## mu =          0.000  -3.032   3.372  
## 
## sigma =  1.000   1.000   1.154   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9708  0.0195  0.0097  
## 
## mu =          0.000   3.059  -3.392  
## 
## sigma =  1.000   1.507   1.202   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9211  0.0440  0.0350  
## 
## mu =          0.000   2.768  -3.047  
## 
## sigma =  1.000   1.355   1.384   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9380  0.0349  0.0271  
## 
## mu =          0.000   2.990  -2.905  
## 
## sigma =  1.000   1.115   1.318   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9889  0.0044  0.0068  
## 
## mu =          0.000  -3.240   3.304  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9545  0.0235  0.0219  
## 
## mu =          0.000  -2.683   3.458  
## 
## sigma =  1.000   1.000   1.446   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9917  0.0016  0.0067  
## 
## mu =          0.000  -1.560   2.802  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9651  0.0117  0.0232  
## 
## mu =          0.000   3.371  -3.207  
## 
## sigma =  1.000   1.117   1.455   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9861  0.0086  0.0052  
## 
## mu =          0.000  -2.806   2.687  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9750  0.0176  0.0074  
## 
## mu =          0.000   2.489  -3.579  
## 
## sigma =  1.000   1.000   1.599   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9603  0.0206  0.0191  
## 
## mu =          0.000   2.865  -3.114  
## 
## sigma =  1.000   1.000   1.114   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9500  0.0248  0.0252  
## 
## mu =          0.000   2.016  -2.630  
## 
## sigma =  1.000   2.283   1.161   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9579  0.0211  0.0209  
## 
## mu =          0.000   3.137  -3.176  
## 
## sigma =  1.000   1.317   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9406  0.0243  0.0351  
## 
## mu =          0.000   2.983  -2.846  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9755  0.0163  0.0081  
## 
## mu =          0.000   2.673  -3.246  
## 
## sigma =  1.000   1.000   1.147   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.944   0.034   0.022   
## 
## mu =          0.000  -2.592   3.549  
## 
## sigma =  1.000   1.247   1.474   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9262  0.0354  0.0384  
## 
## mu =          0.000   2.868  -2.857  
## 
## sigma =  1.000   1.478   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9959  0.0011  0.0030  
## 
## mu =          0.000   0.978  -3.837  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9810  0.0142  0.0048  
## 
## mu =          0.000  -2.397   4.423  
## 
## sigma =  1.000   1.000   2.399   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9983  0.0010  0.0007  
## 
## mu =          0.0000 -0.0783 -3.3539 
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9681  0.0152  0.0168  
## 
## mu =          0.000  -3.057   3.402  
## 
## sigma =  1.000   1.247   1.237   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9179  0.0353  0.0469  
## 
## mu =          0.000  -2.840   2.645  
## 
## sigma =  1.000   1.037   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9498  0.0196  0.0306  
## 
## mu =          0.000   3.059  -2.780  
## 
## sigma =  1.000   1.019   1.283   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9387  0.0271  0.0342  
## 
## mu =          0.000   2.991  -3.145  
## 
## sigma =  1.000   1.025   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9966  0.0015  0.0019  
## 
## mu =          0.000  -2.864   3.398  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9725  0.0151  0.0124  
## 
## mu =          0.000  -2.487   3.172  
## 
## sigma =  1.000   1.000   1.025   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9575  0.0214  0.0210  
## 
## mu =          0.000   2.895  -3.148  
## 
## sigma =  1.000   1.487   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9713  0.0133  0.0154  
## 
## mu =          0.000   2.787  -3.038  
## 
## sigma =  1.000   1.524   1.455   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9637  0.0210  0.0153  
## 
## mu =          0.000   2.752  -3.361  
## 
## sigma =  1.000   1.000   1.237   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9384  0.0260  0.0356  
## 
## mu =          0.000  -2.885   2.748  
## 
## sigma =  1.000   1.303   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9266  0.0347  0.0387  
## 
## mu =          0.000  -2.714   2.864  
## 
## sigma =  1.000   1.315   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9948  0.0021  0.0032  
## 
## mu =          0.000   4.457  -2.420  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9809  0.0095  0.0096  
## 
## mu =          0.000  -3.555   3.221  
## 
## sigma =  1.000   1.000   1.353   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.961   0.015   0.024   
## 
## mu =          0.000   3.010  -3.143  
## 
## sigma =  1.000   1.197   1.056   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9271  0.0284  0.0444  
## 
## mu =          0.000  -2.887   2.463  
## 
## sigma =  1.000   1.266   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9051  0.0454  0.0495  
## 
## mu =          0.000   2.585  -3.210  
## 
## sigma =  1.000   1.145   1.158   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9109  0.0362  0.0529  
## 
## mu =          0.000   3.284  -2.989  
## 
## sigma =  1.000   1.006   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9556  0.0218  0.0227  
## 
## mu =          0.000  -2.905   2.877  
## 
## sigma =  1.000   1.295   1.227   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9766  0.0045  0.0189  
## 
## mu =          0.000  -3.541   2.883  
## 
## sigma =  1.000   1.322   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9880  0.0036  0.0085  
## 
## mu =         0.000   2.887   3.189   
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9892  0.0036  0.0072  
## 
## mu =          0.000  -3.404   3.469  
## 
## sigma =  1.000   1.415   1.859   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9382  0.0331  0.0287  
## 
## mu =          0.000  -2.973   2.946  
## 
## sigma =  1.000   1.393   1.442   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9967  0.0010  0.0023  
## 
## mu =          0.000   6.207  -2.562  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9154  0.0579  0.0267  
## 
## mu =          0.000   3.170  -3.164  
## 
## sigma =  1.000   1.000   1.017   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9307  0.0392  0.0301  
## 
## mu =          0.000   2.790  -2.554  
## 
## sigma =  1.000   1.125   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9969  0.0018  0.0014  
## 
## mu =          0.000   2.143  -1.274  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9911  0.0056  0.0032  
## 
## mu =         0.000   2.207   2.117   
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9866  0.0060  0.0074  
## 
## mu =          0.00   -2.90    3.41   
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9821  0.0142  0.0037  
## 
## mu =          0.000   2.845  -4.000  
## 
## sigma =  1.00    1.00    1.18    
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9877  0.0068  0.0055  
## 
## mu =          0.000   2.721  -2.824  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9945  0.0030  0.0025  
## 
## mu =         0.0000  2.2668  0.8778  
## 
## sigma =  1.000   3.572   3.453   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9750  0.0094  0.0156  
## 
## mu =          0.000  -3.652   3.160  
## 
## sigma =  1.000   1.411   1.377   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9942  0.0040  0.0018  
## 
## mu =         0.0000  1.2871  0.4115  
## 
## sigma =  1.000   4.283   1.385   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9823  0.0059  0.0118  
## 
## mu =          0.000  -3.059   3.261  
## 
## sigma =  1.000   1.000   1.232   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9916  0.0033  0.0051  
## 
## mu =          0.000   3.370  -3.434  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9455  0.0350  0.0195  
## 
## mu =          0.000   2.720  -2.691  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9755  0.0170  0.0074  
## 
## mu =          0.000   3.595  -3.456  
## 
## sigma =  1.000   1.204   1.261   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9823  0.0104  0.0072  
## 
## mu =          0.000   2.939  -3.024  
## 
## sigma =  1.000   1.000   1.127   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9748  0.0117  0.0135  
## 
## mu =          0.000  -3.334   2.721  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9827  0.0070  0.0103  
## 
## mu =          0.000   3.577  -3.805  
## 
## sigma =  1.000   2.408   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9624  0.0182  0.0194  
## 
## mu =          0.000  -2.681   3.093  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9945  0.0020  0.0035  
## 
## mu =          0.000  -5.404   2.826  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9459  0.0202  0.0339  
## 
## mu =          0.000   3.163  -2.994  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9618  0.0130  0.0251  
## 
## mu =          0.000  -2.778   3.407  
## 
## sigma =  1.000   1.088   1.253   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9745  0.0129  0.0125  
## 
## mu =          0.000  -2.922   3.375  
## 
## sigma =  1.000   1.684   1.422   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9778  0.0103  0.0120  
## 
## mu =          0.000  -3.447   3.061  
## 
## sigma =  1.000   2.021   1.425   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9603  0.0212  0.0185  
## 
## mu =          0.000   2.476  -2.891  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9391  0.0272  0.0337  
## 
## mu =          0.000  -3.484   2.990  
## 
## sigma =  1.000   1.641   1.362   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9699  0.0094  0.0207  
## 
## mu =          0.000   3.530  -3.279  
## 
## sigma =  1.000   1.169   1.128   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9798  0.0100  0.0102  
## 
## mu =          0.000   2.980  -3.135  
## 
## sigma =  1.000   1.000   1.017   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9215  0.0382  0.0404  
## 
## mu =          0.000   3.051  -2.865  
## 
## sigma =  1.000   1.109   1.071   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9718  0.0062  0.0220  
## 
## mu =          0.000  -3.166   2.811  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9929  0.0010  0.0061  
## 
## mu =         0.0000  0.1216  3.3177  
## 
## sigma =  1.000   1.000   1.097   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9778  0.0074  0.0148  
## 
## mu =          0.000  -2.514   3.325  
## 
## sigma =  1.00    1.00    1.31    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9728  0.0204  0.0068  
## 
## mu =          0.000  -2.811   3.151  
## 
## sigma =  1.000   1.264   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9412  0.0233  0.0354  
## 
## mu =          0.000  -3.014   3.249  
## 
## sigma =  1.000   1.115   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9837  0.0030  0.0133  
## 
## mu =         0.000   2.753   2.650   
## 
## sigma =  1.000   1.107   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9678  0.0139  0.0182  
## 
## mu =          0.000   2.995  -2.969  
## 
## sigma =  1.000   1.286   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9626  0.0132  0.0242  
## 
## mu =          0.000  -2.996   2.837  
## 
## sigma =  1.000   1.423   1.021   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9451  0.0229  0.0320  
## 
## mu =          0.000  -3.187   2.954  
## 
## sigma =  1.000   1.181   1.437   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9834  0.0061  0.0106  
## 
## mu =          0.000   2.996  -2.736  
## 
## sigma =  1.000   2.003   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9425  0.0213  0.0362  
## 
## mu =          0.000   3.481  -2.939  
## 
## sigma =  1.000   1.226   1.105   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9912  0.0076  0.0011  
## 
## mu =          0.000  -2.996   1.014  
## 
## sigma =  1.000   1.199   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9919  0.0020  0.0061  
## 
## mu =          0.000  -5.185   3.683  
## 
## sigma =  1.000   1.000   1.097   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9778  0.0109  0.0113  
## 
## mu =          0.000  -3.268   2.557  
## 
## sigma =  1.000   1.214   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9132  0.0517  0.0351  
## 
## mu =          0.000   2.871  -2.804  
## 
## sigma =  1.000   1.239   1.473   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9393  0.0356  0.0252  
## 
## mu =          0.000  -2.878   3.171  
## 
## sigma =  1.000   1.259   1.208   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9376  0.0438  0.0186  
## 
## mu =          0.000  -2.801   3.197  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9622  0.0178  0.0199  
## 
## mu =          0.000  -2.861   3.339  
## 
## sigma =  1.000   1.000   1.534   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9549  0.0218  0.0232  
## 
## mu =          0.000  -2.868   2.775  
## 
## sigma =  1.000   1.000   1.039   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9642  0.0162  0.0197  
## 
## mu =          0.000  -2.834   2.863  
## 
## sigma =  1.000   1.116   1.090   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9464  0.0310  0.0226  
## 
## mu =          0.000   2.890  -2.669  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9874  0.0091  0.0034  
## 
## mu =          0.000   3.473  -3.207  
## 
## sigma =  1.000   1.000   1.125   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9892  0.0064  0.0044  
## 
## mu =          0.000  -3.773   3.982  
## 
## sigma =  1.000   1.000   1.831   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9332  0.0318  0.0350  
## 
## mu =          0.000  -3.045   2.888  
## 
## sigma =  1.000   1.143   1.003   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9023  0.0500  0.0477  
## 
## mu =          0.000   3.108  -2.790  
## 
## sigma =  1.000   1.373   1.567   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9471  0.0274  0.0255  
## 
## mu =          0.000  -3.434   2.729  
## 
## sigma =  1.000   1.001   1.053   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9950  0.0019  0.0031  
## 
## mu =          0.000  -2.499  -2.646  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9951  0.0028  0.0021  
## 
## mu =          0.000  -4.137   3.900  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9645  0.0141  0.0214  
## 
## mu =          0.000  -3.064   2.670  
## 
## sigma =  1.000   1.529   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9502  0.0211  0.0287  
## 
## mu =          0.000  -3.202   2.971  
## 
## sigma =  1.000   1.306   1.015   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9710  0.0165  0.0125  
## 
## mu =          0.000  -3.113   3.008  
## 
## sigma =  1.000   1.422   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9709  0.0106  0.0184  
## 
## mu =          0.000  -3.154   2.755  
## 
## sigma =  1.000   1.106   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9444  0.0311  0.0245  
## 
## mu =          0.000   3.582  -2.892  
## 
## sigma =  1.000   1.194   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9457  0.0325  0.0218  
## 
## mu =          0.000  -3.063   2.998  
## 
## sigma =  1.000   1.023   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9675  0.0174  0.0150  
## 
## mu =          0.000  -2.744   3.837  
## 
## sigma =  1.000   1.091   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9927  0.0024  0.0050  
## 
## mu =          0.000   2.292  -2.804  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9877  0.0044  0.0079  
## 
## mu =          0.000   2.904  -3.694  
## 
## sigma =  1.00    1.04    1.00    
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9174  0.0395  0.0431  
## 
## mu =          0.000   2.645  -2.642  
## 
## sigma =  1.000   1.000   1.152   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9718  0.0135  0.0148  
## 
## mu =          0.000  -3.483   2.892  
## 
## sigma =  1.000   1.153   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9511  0.0250  0.0240  
## 
## mu =          0.000   2.778  -2.802  
## 
## sigma =  1.000   1.000   1.201   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9891  0.0019  0.0090  
## 
## mu =          0.000  -5.083   3.563  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9278  0.0282  0.0440  
## 
## mu =          0.000  -2.867   2.964  
## 
## sigma =  1.000   1.115   1.294   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9834  0.0077  0.0089  
## 
## mu =          0.000   2.731  -3.335  
## 
## sigma =  1.000   1.000   1.272   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9354  0.0230  0.0417  
## 
## mu =          0.000  -3.015   2.530  
## 
## sigma =  1.000   1.456   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9775  0.0138  0.0087  
## 
## mu =          0.000   3.394  -3.526  
## 
## sigma =  1.000   1.034   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9560  0.0251  0.0190  
## 
## mu =          0.000   2.973  -2.960  
## 
## sigma =  1.000   1.227   1.084   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9700  0.0138  0.0162  
## 
## mu =          0.000  -2.652   2.787  
## 
## sigma =  1.000   1.255   1.397   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9877  0.0042  0.0081  
## 
## mu =          0.000  -4.178   3.056  
## 
## sigma =  1.000   1.000   1.388   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9153  0.0528  0.0319  
## 
## mu =          0.000  -2.963   3.072  
## 
## sigma =  1.000   1.215   1.125   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9286  0.0309  0.0406  
## 
## mu =          0.000  -2.678   2.735  
## 
## sigma =  1.000   1.000   1.554   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9703  0.0142  0.0156  
## 
## mu =          0.000   2.982  -2.956  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9892  0.0076  0.0032  
## 
## mu =          0.000   3.047  -2.686  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9709  0.0161  0.0130  
## 
## mu =          0.000  -2.529   2.488  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9383  0.0309  0.0308  
## 
## mu =          0.000   3.174  -3.154  
## 
## sigma =  1.000   1.353   1.167   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9454  0.0309  0.0236  
## 
## mu =          0.000  -2.780   3.206  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9825  0.0113  0.0062  
## 
## mu =          0.000  -3.812   3.558  
## 
## sigma =  1.000   1.363   1.644   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9309  0.0348  0.0344  
## 
## mu =          0.000   2.827  -3.098  
## 
## sigma =  1.000   1.071   1.127   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9929  0.0027  0.0044  
## 
## mu =          0.000  -3.328   3.042  
## 
## sigma =  1.000   1.612   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9964  0.0017  0.0019  
## 
## mu =          0.000  -4.213   3.195  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9456  0.0214  0.0330  
## 
## mu =          0.000  -2.996   2.873  
## 
## sigma =  1.000   1.000   1.137   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9902  0.0051  0.0047  
## 
## mu =          0.000   2.524  -2.516  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9887  0.0023  0.0090  
## 
## mu =          0.000  -4.542   2.408  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9573  0.0171  0.0256  
## 
## mu =          0.000   2.971  -2.924  
## 
## sigma =  1.000   1.000   1.266   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9910  0.0071  0.0019  
## 
## mu =          0.000  -2.567   2.257  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9417  0.0307  0.0276  
## 
## mu =          0.000  -2.914   3.100  
## 
## sigma =  1.00    1.05    1.00    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9936  0.0029  0.0036  
## 
## mu =          0.000   4.636  -4.455  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9936  0.0039  0.0024  
## 
## mu =          0.000   2.695  -3.872  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9187  0.0440  0.0373  
## 
## mu =          0.000   2.790  -2.988  
## 
## sigma =  1.000   1.171   1.166   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9628  0.0131  0.0241  
## 
## mu =          0.000  -3.212   3.211  
## 
## sigma =  1.000   1.765   1.667   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9910  0.0034  0.0056  
## 
## mu =          0.000   3.533  -4.147  
## 
## sigma =  1.000   1.578   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9652  0.0133  0.0215  
## 
## mu =          0.000   3.083  -3.081  
## 
## sigma =  1.000   1.293   1.527   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9974  0.0016  0.0011  
## 
## mu =          0.0000 -3.2379  0.3495 
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9684  0.0165  0.0151  
## 
## mu =          0.000   3.081  -3.444  
## 
## sigma =  1.000   1.292   1.133   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9973  0.0012  0.0015  
## 
## mu =          0.000  -4.514   1.437  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9348  0.0240  0.0412  
## 
## mu =          0.000  -3.007   2.587  
## 
## sigma =  1.000   2.266   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9822  0.0069  0.0109  
## 
## mu =          0.000   3.529  -3.241  
## 
## sigma =  1.000   1.446   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9649  0.0184  0.0167  
## 
## mu =          0.000   3.453  -2.470  
## 
## sigma =  1.000   1.000   2.387   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.996   0.003   0.001   
## 
## mu =          0.000  -2.752   7.333  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9499  0.0251  0.0249  
## 
## mu =          0.000   3.151  -2.952  
## 
## sigma =  1.000   1.275   1.172   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9554  0.0238  0.0208  
## 
## mu =          0.000   3.055  -3.009  
## 
## sigma =  1.000   1.553   1.250   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9529  0.0221  0.0250  
## 
## mu =          0.000   3.005  -2.517  
## 
## sigma =  1.000   1.278   1.509   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9728  0.0111  0.0161  
## 
## mu =          0.000   3.247  -2.860  
## 
## sigma =  1.000   1.103   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9717  0.0119  0.0164  
## 
## mu =          0.000  -2.676   3.320  
## 
## sigma =  1.000   1.000   1.139   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9434  0.0338  0.0228  
## 
## mu =          0.000  -2.761   3.005  
## 
## sigma =  1.00    1.00    1.27    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9780  0.0128  0.0092  
## 
## mu =          0.000  -3.162   3.198  
## 
## sigma =  1.000   1.164   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9203  0.0269  0.0528  
## 
## mu =          0.000   3.334  -2.768  
## 
## sigma =  1.000   1.197   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9466  0.0244  0.0290  
## 
## mu =          0.000   2.922  -3.143  
## 
## sigma =  1.000   1.093   1.430   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9159  0.0379  0.0463  
## 
## mu =          0.000  -2.643   3.061  
## 
## sigma =  1.000   1.060   1.331   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9683  0.0125  0.0191  
## 
## mu =          0.000  -2.998   3.052  
## 
## sigma =  1.000   1.246   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9347  0.0307  0.0346  
## 
## mu =          0.000  -3.183   2.735  
## 
## sigma =  1.000   1.083   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Using an empirical null with a fitted noiseSD gives a
## substantially different model. Consider rerunning with theonull = FALSE
## and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9081  0.0445  0.0474  
## 
## mu =          0.000   2.848  -2.761  
## 
## sigma =  1.000   1.024   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9805  0.0085  0.0110  
## 
## mu =          0.000  -2.866   2.683  
## 
## sigma =  1.000   1.341   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9076  0.0549  0.0375  
## 
## mu =          0.000  -2.626   3.083  
## 
## sigma =  1.000   1.000   1.341   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9731  0.0132  0.0137  
## 
## mu =          0.000  -3.257   2.686  
## 
## sigma =  1.000   1.148   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9976  0.0013  0.0011  
## 
## mu =          0.000  -2.842  -1.161  
## 
## sigma =  1.00    1.00    1.32    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9792  0.0120  0.0088  
## 
## mu =          0.000   2.985  -2.922  
## 
## sigma =  1.000   1.258   1.000   
## 
## noiseSD =    1   
simres1a = basicsim(mixsd, mixpi_alt, niter = 200, nsamp = 10000)
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9456  0.0253  0.0292  
## 
## mu =          0.000   2.987  -3.008  
## 
## sigma =  1.000   1.214   1.373   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9369  0.0308  0.0323  
## 
## mu =          0.000   2.888  -2.878  
## 
## sigma =  1.000   1.082   1.190   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9789  0.0105  0.0106  
## 
## mu =          0.000  -2.816   2.476  
## 
## sigma =  1.000   1.160   1.663   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9659  0.0186  0.0154  
## 
## mu =          0.000   2.774  -2.981  
## 
## sigma =  1.000   1.304   1.229   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9300  0.0341  0.0360  
## 
## mu =          0.000  -3.048   2.899  
## 
## sigma =  1.000   1.228   1.047   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9209  0.0405  0.0386  
## 
## mu =          0.000   2.949  -2.835  
## 
## sigma =  1.000   1.258   1.204   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9861  0.0073  0.0067  
## 
## mu =          0.000   2.128  -2.488  
## 
## sigma =  1.000   1.996   1.187   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9289  0.0363  0.0348  
## 
## mu =          0.000   2.872  -2.867  
## 
## sigma =  1.000   1.217   1.072   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9354  0.0309  0.0337  
## 
## mu =          0.000   2.937  -2.991  
## 
## sigma =  1.000   1.240   1.141   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9854  0.0073  0.0073  
## 
## mu =          0.000  -2.791   2.359  
## 
## sigma =  1.000   1.129   1.155   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9249  0.0400  0.0352  
## 
## mu =          0.000   2.689  -2.925  
## 
## sigma =  1.000   1.252   1.164   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9729  0.0135  0.0136  
## 
## mu =          0.000  -2.795   3.115  
## 
## sigma =  1.000   1.513   1.242   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9661  0.0185  0.0154  
## 
## mu =          0.000   2.886  -2.718  
## 
## sigma =  1.000   1.415   1.169   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9555  0.0201  0.0244  
## 
## mu =          0.000   2.952  -2.763  
## 
## sigma =  1.000   1.274   1.201   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9854  0.0074  0.0072  
## 
## mu =          0.000   2.664  -1.569  
## 
## sigma =  1.000   1.345   2.330   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9873  0.0057  0.0070  
## 
## mu =          0.000  -2.536   2.697  
## 
## sigma =  1.000   1.563   1.383   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9422  0.0254  0.0324  
## 
## mu =          0.000   3.036  -2.893  
## 
## sigma =  1.000   1.279   1.128   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9825  0.0089  0.0087  
## 
## mu =          0.000  -2.834   2.986  
## 
## sigma =  1.000   1.197   1.429   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9398  0.0318  0.0284  
## 
## mu =          0.000  -2.913   3.009  
## 
## sigma =  1.000   1.107   1.206   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9864  0.0054  0.0081  
## 
## mu =          0.000  -2.338   2.520  
## 
## sigma =  1.000   1.799   1.337   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9396  0.0287  0.0317  
## 
## mu =          0.000   2.865  -2.770  
## 
## sigma =  1.000   1.084   1.355   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9868  0.0066  0.0067  
## 
## mu =          0.000  -2.393   2.751  
## 
## sigma =  1.000   1.510   1.299   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9569  0.0223  0.0209  
## 
## mu =          0.000   3.010  -2.979  
## 
## sigma =  1.000   1.154   1.123   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9621  0.0203  0.0176  
## 
## mu =          0.000   3.212  -3.092  
## 
## sigma =  1.000   1.249   1.268   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9510  0.0238  0.0252  
## 
## mu =          0.000   2.927  -2.797  
## 
## sigma =  1.000   1.248   1.159   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9653  0.0184  0.0163  
## 
## mu =          0.000  -2.986   3.160  
## 
## sigma =  1.000   1.297   1.244   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9341  0.0321  0.0338  
## 
## mu =          0.000   3.003  -2.614  
## 
## sigma =  1.000   1.279   1.264   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9793  0.0096  0.0111  
## 
## mu =          0.000  -2.858   2.930  
## 
## sigma =  1.000   1.198   1.456   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9621  0.0200  0.0179  
## 
## mu =          0.000   2.976  -2.948  
## 
## sigma =  1.000   1.271   1.250   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9832  0.0083  0.0085  
## 
## mu =          0.000   2.718  -2.696  
## 
## sigma =  1.000   1.378   1.154   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9610  0.0172  0.0218  
## 
## mu =          0.000  -2.942   2.884  
## 
## sigma =  1.000   1.208   1.196   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE    TRUE   
## 
## pi =     0.9885  0.0061  0.0054  
## 
## mu =          0.0000 -0.4364 -0.0224 
## 
## sigma =  1.000   2.194   1.252   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9409  0.0300  0.0291  
## 
## mu =          0.000   2.946  -3.000  
## 
## sigma =  1.000   1.187   1.067   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.919   0.044   0.037   
## 
## mu =          0.000   2.775  -2.918  
## 
## sigma =  1.000   1.203   1.300   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9098  0.0473  0.0429  
## 
## mu =          0.000   2.876  -2.945  
## 
## sigma =  1.000   1.197   1.194   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9465  0.0271  0.0264  
## 
## mu =          0.000   2.844  -2.874  
## 
## sigma =  1.000   1.290   1.099   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9778  0.0102  0.0120  
## 
## mu =          0.000   3.047  -2.880  
## 
## sigma =  1.000   1.308   1.409   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9533  0.0236  0.0231  
## 
## mu =          0.000  -2.815   3.017  
## 
## sigma =  1.000   1.212   1.115   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9898  0.0051  0.0051  
## 
## mu =          0.0000 -0.5848  1.2232 
## 
## sigma =  1.000   1.023   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9301  0.0352  0.0347  
## 
## mu =          0.000  -2.876   2.874  
## 
## sigma =  1.000   1.262   1.182   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9373  0.0312  0.0315  
## 
## mu =          0.000   2.922  -2.946  
## 
## sigma =  1.000   1.105   1.082   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9216  0.0379  0.0405  
## 
## mu =          0.000  -2.866   2.937  
## 
## sigma =  1.000   1.049   1.178   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9592  0.0184  0.0224  
## 
## mu =          0.000  -3.133   2.870  
## 
## sigma =  1.000   1.100   1.223   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9375  0.0285  0.0340  
## 
## mu =          0.000   2.931  -2.731  
## 
## sigma =  1.000   1.108   1.290   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9845  0.0076  0.0078  
## 
## mu =          0.000  -2.564   2.887  
## 
## sigma =  1.000   1.807   1.454   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9798  0.0096  0.0105  
## 
## mu =          0.000  -3.206   2.676  
## 
## sigma =  1.000   1.148   1.300   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9887  0.0056  0.0057  
## 
## mu =          0.000  -2.266   2.434  
## 
## sigma =  1.000   1.235   1.869   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9798  0.0100  0.0101  
## 
## mu =          0.000  -2.737   3.000  
## 
## sigma =  1.00    1.30    1.31    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9655  0.0199  0.0147  
## 
## mu =          0.000  -3.020   2.891  
## 
## sigma =  1.000   1.174   1.176   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9586  0.0221  0.0193  
## 
## mu =          0.000  -2.752   2.966  
## 
## sigma =  1.000   1.441   1.263   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9218  0.0385  0.0397  
## 
## mu =          0.000   2.813  -2.724  
## 
## sigma =  1.000   1.248   1.312   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9270  0.0363  0.0366  
## 
## mu =          0.000  -2.954   2.895  
## 
## sigma =  1.000   1.211   1.230   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9687  0.0162  0.0150  
## 
## mu =          0.000  -3.037   2.887  
## 
## sigma =  1.000   1.131   1.337   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9348  0.0326  0.0326  
## 
## mu =          0.000  -2.941   2.914  
## 
## sigma =  1.000   1.114   1.207   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9899  0.0051  0.0050  
## 
## mu =         0.0000  0.0999  0.9951  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9770  0.0108  0.0122  
## 
## mu =          0.000  -3.091   2.749  
## 
## sigma =  1.000   1.221   1.255   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9832  0.0092  0.0076  
## 
## mu =          0.000   2.743  -2.787  
## 
## sigma =  1.000   1.355   1.236   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9505  0.0255  0.0240  
## 
## mu =          0.000   2.942  -3.062  
## 
## sigma =  1.000   1.159   1.169   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9486  0.0259  0.0255  
## 
## mu =          0.000   3.002  -2.912  
## 
## sigma =  1.000   1.237   1.151   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9310  0.0362  0.0328  
## 
## mu =          0.000   2.848  -2.908  
## 
## sigma =  1.000   1.165   1.157   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9862  0.0076  0.0063  
## 
## mu =          0.00   -2.26    1.13   
## 
## sigma =  1.000   1.196   2.219   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9761  0.0099  0.0140  
## 
## mu =          0.000   3.246  -2.912  
## 
## sigma =  1.000   1.142   1.077   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9809  0.0098  0.0093  
## 
## mu =          0.000   2.447  -2.779  
## 
## sigma =  1.000   1.609   1.232   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9786  0.0103  0.0112  
## 
## mu =          0.000  -2.584   3.007  
## 
## sigma =  1.000   1.482   1.212   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9235  0.0407  0.0359  
## 
## mu =          0.000  -2.692   2.705  
## 
## sigma =  1.000   1.273   1.256   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9497  0.0236  0.0267  
## 
## mu =          0.000  -2.918   2.685  
## 
## sigma =  1.000   1.277   1.259   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9789  0.0101  0.0111  
## 
## mu =          0.000   2.444  -2.780  
## 
## sigma =  1.000   1.301   1.358   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9405  0.0289  0.0305  
## 
## mu =          0.000   3.078  -2.882  
## 
## sigma =  1.000   1.119   1.109   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9217  0.0383  0.0400  
## 
## mu =          0.000   2.841  -2.926  
## 
## sigma =  1.000   1.127   1.115   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9806  0.0094  0.0100  
## 
## mu =          0.000  -2.922   2.695  
## 
## sigma =  1.000   1.212   1.763   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9894  0.0053  0.0053  
## 
## mu =          0.0000 -0.1583 -0.3238 
## 
## sigma =  1.000   1.000   2.074   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9520  0.0272  0.0208  
## 
## mu =          0.000   2.927  -2.965  
## 
## sigma =  1.000   1.138   1.171   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9880  0.0059  0.0061  
## 
## mu =          0.000  -2.536   2.341  
## 
## sigma =  1.000   2.000   1.334   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9452  0.0292  0.0255  
## 
## mu =          0.000  -2.793   2.876  
## 
## sigma =  1.000   1.232   1.157   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9616  0.0215  0.0169  
## 
## mu =          0.000  -2.635   2.937  
## 
## sigma =  1.000   1.202   1.265   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9205  0.0394  0.0401  
## 
## mu =          0.000   2.769  -2.742  
## 
## sigma =  1.000   1.146   1.180   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9685  0.0159  0.0156  
## 
## mu =          0.000  -2.904   3.014  
## 
## sigma =  1.000   1.177   1.474   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9578  0.0210  0.0212  
## 
## mu =          0.000  -2.796   2.716  
## 
## sigma =  1.000   1.347   1.250   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9550  0.0214  0.0236  
## 
## mu =          0.000  -2.873   2.841  
## 
## sigma =  1.000   1.159   1.123   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9641  0.0178  0.0181  
## 
## mu =          0.000   2.937  -2.778  
## 
## sigma =  1.000   1.323   1.389   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9495  0.0244  0.0261  
## 
## mu =          0.000   3.095  -2.949  
## 
## sigma =  1.000   1.243   1.175   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9266  0.0402  0.0333  
## 
## mu =          0.000  -2.906   2.977  
## 
## sigma =  1.000   1.269   1.179   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9128  0.0412  0.0460  
## 
## mu =          0.000  -2.924   2.853  
## 
## sigma =  1.000   1.172   1.159   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9731  0.0129  0.0140  
## 
## mu =          0.000   2.864  -2.807  
## 
## sigma =  1.000   1.315   1.331   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9375  0.0318  0.0307  
## 
## mu =          0.000  -2.915   2.966  
## 
## sigma =  1.000   1.245   1.135   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9254  0.0352  0.0394  
## 
## mu =          0.000  -2.795   2.814  
## 
## sigma =  1.000   1.201   1.232   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9309  0.0346  0.0345  
## 
## mu =          0.000   2.899  -2.868  
## 
## sigma =  1.000   1.160   1.168   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9815  0.0094  0.0091  
## 
## mu =          0.000   2.463  -3.006  
## 
## sigma =  1.000   1.496   1.306   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9170  0.0352  0.0479  
## 
## mu =          0.000  -2.999   2.804  
## 
## sigma =  1.000   1.240   1.204   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9686  0.0155  0.0159  
## 
## mu =          0.000  -2.901   2.990  
## 
## sigma =  1.000   1.606   1.170   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9381  0.0300  0.0319  
## 
## mu =          0.000  -3.034   2.962  
## 
## sigma =  1.000   1.145   1.192   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9914  0.0038  0.0048  
## 
## mu =          0.0000  0.5603 -0.8404 
## 
## sigma =  1.000   2.302   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9740  0.0131  0.0129  
## 
## mu =          0.000  -2.892   2.811  
## 
## sigma =  1.000   1.438   1.361   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9500  0.0247  0.0253  
## 
## mu =          0.000   2.951  -2.905  
## 
## sigma =  1.000   1.176   1.309   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9862  0.0063  0.0075  
## 
## mu =          0.000  -2.196   2.511  
## 
## sigma =  1.000   1.025   1.306   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9530  0.0257  0.0214  
## 
## mu =          0.000  -2.710   2.952  
## 
## sigma =  1.000   1.297   1.210   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.953   0.023   0.024   
## 
## mu =          0.000  -2.951   3.039  
## 
## sigma =  1.000   1.241   1.237   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9847  0.0086  0.0067  
## 
## mu =          0.000   2.930  -2.752  
## 
## sigma =  1.000   1.318   1.215   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9807  0.0104  0.0089  
## 
## mu =          0.000   2.759  -2.802  
## 
## sigma =  1.000   1.350   1.204   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9677  0.0153  0.0170  
## 
## mu =          0.000   2.920  -2.986  
## 
## sigma =  1.000   1.209   1.350   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9182  0.0417  0.0401  
## 
## mu =          0.000   2.950  -2.918  
## 
## sigma =  1.000   1.041   1.124   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9108  0.0414  0.0478  
## 
## mu =          0.000   2.918  -2.906  
## 
## sigma =  1.000   1.054   1.167   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9447  0.0291  0.0262  
## 
## mu =          0.000  -3.066   2.941  
## 
## sigma =  1.000   1.161   1.009   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE    TRUE   
## 
## pi =     0.9883  0.0059  0.0058  
## 
## mu =          0.0000  0.5146 -0.1534 
## 
## sigma =  1.000   2.091   2.804   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9879  0.0058  0.0064  
## 
## mu =          0.000  -1.287   1.352  
## 
## sigma =  1.000   1.840   1.727   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9578  0.0207  0.0216  
## 
## mu =          0.000   2.755  -2.985  
## 
## sigma =  1.000   1.228   1.244   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9330  0.0354  0.0316  
## 
## mu =          0.000   3.019  -2.935  
## 
## sigma =  1.000   1.167   1.127   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9689  0.0142  0.0169  
## 
## mu =          0.000  -2.932   2.826  
## 
## sigma =  1.000   1.415   1.196   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9248  0.0386  0.0366  
## 
## mu =          0.000  -2.851   3.008  
## 
## sigma =  1.000   1.148   1.329   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9864  0.0071  0.0065  
## 
## mu =          0.000  -2.458   2.536  
## 
## sigma =  1.000   1.525   1.099   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9194  0.0394  0.0412  
## 
## mu =          0.000  -2.850   2.841  
## 
## sigma =  1.000   1.126   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9756  0.0117  0.0126  
## 
## mu =          0.000  -3.083   2.763  
## 
## sigma =  1.000   1.189   1.291   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9241  0.0388  0.0371  
## 
## mu =          0.000   2.878  -2.915  
## 
## sigma =  1.000   1.176   1.260   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9872  0.0061  0.0066  
## 
## mu =          0.000   2.757  -1.620  
## 
## sigma =  1.000   1.290   1.685   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9233  0.0402  0.0364  
## 
## mu =          0.000   2.945  -2.884  
## 
## sigma =  1.000   1.130   1.148   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9355  0.0296  0.0348  
## 
## mu =          0.000  -2.823   2.904  
## 
## sigma =  1.000   1.239   1.232   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9653  0.0164  0.0183  
## 
## mu =          0.000  -3.060   2.973  
## 
## sigma =  1.000   1.193   1.100   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9720  0.0162  0.0119  
## 
## mu =          0.000   2.820  -2.784  
## 
## sigma =  1.000   1.526   1.045   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9685  0.0149  0.0166  
## 
## mu =          0.000  -2.997   2.725  
## 
## sigma =  1.000   1.415   1.296   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9707  0.0138  0.0155  
## 
## mu =          0.000   2.959  -2.802  
## 
## sigma =  1.000   1.311   1.380   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9316  0.0341  0.0343  
## 
## mu =          0.000   2.912  -2.972  
## 
## sigma =  1.000   1.107   1.087   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9234  0.0381  0.0385  
## 
## mu =          0.000   2.938  -2.827  
## 
## sigma =  1.000   1.204   1.014   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9316  0.0336  0.0348  
## 
## mu =          0.000   2.763  -2.829  
## 
## sigma =  1.000   1.271   1.221   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9479  0.0259  0.0262  
## 
## mu =          0.000   3.074  -3.015  
## 
## sigma =  1.000   1.147   1.206   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9221  0.0384  0.0395  
## 
## mu =          0.000   2.847  -2.924  
## 
## sigma =  1.000   1.183   1.185   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9854  0.0084  0.0063  
## 
## mu =          0.000   2.652  -2.248  
## 
## sigma =  1.000   1.000   1.027   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9428  0.0276  0.0296  
## 
## mu =          0.000   3.029  -3.030  
## 
## sigma =  1.000   1.144   1.018   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9805  0.0098  0.0097  
## 
## mu =          0.000   2.436  -2.943  
## 
## sigma =  1.000   1.758   1.110   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9486  0.0257  0.0257  
## 
## mu =          0.000   2.845  -2.933  
## 
## sigma =  1.000   1.434   1.310   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.978   0.012   0.010   
## 
## mu =          0.000  -2.882   3.073  
## 
## sigma =  1.000   1.179   1.368   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9777  0.0116  0.0107  
## 
## mu =          0.000  -2.976   3.158  
## 
## sigma =  1.000   1.331   1.332   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9466  0.0249  0.0285  
## 
## mu =          0.000   3.216  -2.876  
## 
## sigma =  1.000   1.238   1.091   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9545  0.0226  0.0229  
## 
## mu =          0.000  -3.030   2.989  
## 
## sigma =  1.000   1.270   1.192   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9741  0.0116  0.0143  
## 
## mu =          0.000  -2.694   2.850  
## 
## sigma =  1.000   1.502   1.000   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9465  0.0266  0.0269  
## 
## mu =          0.000  -2.908   2.672  
## 
## sigma =  1.000   1.022   1.445   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9650  0.0162  0.0188  
## 
## mu =          0.000  -2.831   2.822  
## 
## sigma =  1.000   1.334   1.320   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9266  0.0389  0.0345  
## 
## mu =          0.000   2.877  -2.874  
## 
## sigma =  1.000   1.301   1.198   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9750  0.0112  0.0137  
## 
## mu =          0.000   2.960  -2.739  
## 
## sigma =  1.000   1.203   1.100   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9587  0.0213  0.0200  
## 
## mu =          0.000   2.855  -3.079  
## 
## sigma =  1.000   1.268   1.079   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9776  0.0104  0.0120  
## 
## mu =          0.000   2.983  -2.736  
## 
## sigma =  1.000   1.285   1.231   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9573  0.0214  0.0213  
## 
## mu =          0.000   2.874  -2.649  
## 
## sigma =  1.000   1.318   1.458   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9894  0.0055  0.0051  
## 
## mu =         0.0000  0.1537  0.4573  
## 
## sigma =  1.000   2.661   1.309   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9175  0.0396  0.0428  
## 
## mu =          0.000  -2.990   2.828  
## 
## sigma =  1.000   1.142   1.198   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9865  0.0068  0.0066  
## 
## mu =          0.000  -2.544   2.506  
## 
## sigma =  1.000   1.797   1.833   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9275  0.0355  0.0370  
## 
## mu =          0.000   3.061  -2.864  
## 
## sigma =  1.000   1.128   1.355   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9858  0.0069  0.0073  
## 
## mu =          0.000  -2.072   2.341  
## 
## sigma =  1.000   1.727   1.466   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9544  0.0228  0.0228  
## 
## mu =          0.000   2.775  -2.977  
## 
## sigma =  1.000   1.228   1.190   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.985   0.007   0.008   
## 
## mu =          0.000  -2.252   2.815  
## 
## sigma =  1.000   1.202   1.119   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9875  0.0072  0.0053  
## 
## mu =          0.000   2.271  -2.545  
## 
## sigma =  1.000   1.698   1.470   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9874  0.0060  0.0066  
## 
## mu =          0.0000 -0.9812  1.7210 
## 
## sigma =  1.000   2.227   1.698   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9800  0.0095  0.0104  
## 
## mu =          0.000  -2.762   2.582  
## 
## sigma =  1.000   1.439   1.121   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9131  0.0458  0.0411  
## 
## mu =          0.000   2.872  -2.907  
## 
## sigma =  1.000   1.135   1.150   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9408  0.0305  0.0286  
## 
## mu =          0.000  -3.053   2.968  
## 
## sigma =  1.000   1.210   1.202   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9821  0.0095  0.0085  
## 
## mu =          0.000  -2.447   2.859  
## 
## sigma =  1.000   1.123   1.365   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9263  0.0343  0.0394  
## 
## mu =          0.000  -2.848   2.839  
## 
## sigma =  1.000   1.111   1.141   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9445  0.0270  0.0286  
## 
## mu =          0.000   2.830  -2.947  
## 
## sigma =  1.000   1.245   1.196   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9598  0.0214  0.0188  
## 
## mu =          0.000  -2.689   2.976  
## 
## sigma =  1.000   1.304   1.242   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9885  0.0054  0.0061  
## 
## mu =          0.0000 -0.4952  1.6357 
## 
## sigma =  1.000   2.272   1.474   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9896  0.0053  0.0051  
## 
## mu =          0.0000  0.3547 -1.4221 
## 
## sigma =  1.000   1.059   1.241   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9852  0.0068  0.0080  
## 
## mu =          0.000   2.926  -2.458  
## 
## sigma =  1.000   1.382   1.240   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9467  0.0258  0.0275  
## 
## mu =          0.00    2.83   -2.83   
## 
## sigma =  1.000   1.285   1.257   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9605  0.0213  0.0182  
## 
## mu =          0.000   3.084  -2.829  
## 
## sigma =  1.000   1.000   1.371   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9356  0.0324  0.0320  
## 
## mu =          0.000   2.736  -2.818  
## 
## sigma =  1.000   1.239   1.153   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9674  0.0166  0.0160  
## 
## mu =          0.000  -2.820   2.918  
## 
## sigma =  1.000   1.104   1.338   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9249  0.0369  0.0382  
## 
## mu =          0.000  -2.987   2.954  
## 
## sigma =  1.000   1.173   1.176   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9689  0.0162  0.0150  
## 
## mu =          0.000   2.716  -2.716  
## 
## sigma =  1.000   1.423   1.205   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9323  0.0332  0.0346  
## 
## mu =          0.000  -2.932   2.897  
## 
## sigma =  1.000   1.185   1.125   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9816  0.0096  0.0087  
## 
## mu =          0.000   2.842  -2.393  
## 
## sigma =  1.000   1.146   1.810   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9876  0.0062  0.0062  
## 
## mu =          0.000  -2.165   2.055  
## 
## sigma =  1.000   1.357   1.649   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9223  0.0389  0.0387  
## 
## mu =          0.000   2.792  -2.937  
## 
## sigma =  1.000   1.125   1.185   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9692  0.0151  0.0157  
## 
## mu =          0.000   2.693  -2.995  
## 
## sigma =  1.000   1.371   1.425   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9332  0.0334  0.0334  
## 
## mu =          0.000  -2.929   2.936  
## 
## sigma =  1.000   1.206   1.129   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9762  0.0124  0.0113  
## 
## mu =          0.000  -2.915   2.938  
## 
## sigma =  1.000   1.141   1.326   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9461  0.0253  0.0286  
## 
## mu =          0.000   3.025  -2.958  
## 
## sigma =  1.000   1.285   1.133   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9157  0.0449  0.0393  
## 
## mu =          0.000  -2.897   2.836  
## 
## sigma =  1.000   1.190   1.181   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9144  0.0452  0.0404  
## 
## mu =          0.000  -2.881   2.916  
## 
## sigma =  1.000   1.189   1.128   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9114  0.0452  0.0435  
## 
## mu =          0.000   2.694  -2.737  
## 
## sigma =  1.000   1.190   1.229   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9712  0.0134  0.0153  
## 
## mu =          0.000   2.929  -2.848  
## 
## sigma =  1.000   1.201   1.409   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9224  0.0391  0.0386  
## 
## mu =          0.000   2.781  -2.792  
## 
## sigma =  1.000   1.287   1.230   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9797  0.0098  0.0105  
## 
## mu =          0.000  -3.129   2.853  
## 
## sigma =  1.000   1.103   1.404   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9855  0.0078  0.0067  
## 
## mu =          0.000  -2.361   3.016  
## 
## sigma =  1.000   1.259   1.176   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9074  0.0449  0.0476  
## 
## mu =          0.000   2.702  -2.777  
## 
## sigma =  1.000   1.364   1.213   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9450  0.0297  0.0253  
## 
## mu =          0.000   2.892  -2.965  
## 
## sigma =  1.000   1.223   1.040   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9716  0.0140  0.0144  
## 
## mu =          0.000   2.923  -3.076  
## 
## sigma =  1.000   1.228   1.046   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9806  0.0087  0.0107  
## 
## mu =          0.000  -2.815   2.592  
## 
## sigma =  1.000   1.246   1.708   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9137  0.0419  0.0445  
## 
## mu =          0.000   2.787  -2.957  
## 
## sigma =  1.000   1.192   1.169   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9565  0.0226  0.0210  
## 
## mu =          0.000   2.968  -3.093  
## 
## sigma =  1.000   1.210   1.249   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9859  0.0069  0.0072  
## 
## mu =          0.000   2.064  -2.565  
## 
## sigma =  1.000   1.660   1.024   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9713  0.0145  0.0142  
## 
## mu =          0.000  -2.766   2.807  
## 
## sigma =  1.000   1.179   1.032   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9748  0.0120  0.0132  
## 
## mu =          0.000  -3.271   2.780  
## 
## sigma =  1.000   1.000   1.435   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9369  0.0293  0.0338  
## 
## mu =          0.000  -2.921   2.835  
## 
## sigma =  1.000   1.129   1.306   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9316  0.0356  0.0328  
## 
## mu =          0.000  -2.819   2.985  
## 
## sigma =  1.000   1.208   1.305   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9217  0.0413  0.0370  
## 
## mu =          0.000  -2.791   2.683  
## 
## sigma =  1.000   1.113   1.328   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9265  0.0328  0.0407  
## 
## mu =          0.000   2.828  -2.934  
## 
## sigma =  1.000   1.237   1.079   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9763  0.0112  0.0125  
## 
## mu =          0.000  -2.855   2.890  
## 
## sigma =  1.00    1.36    1.29    
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9122  0.0475  0.0403  
## 
## mu =          0.000   2.687  -2.861  
## 
## sigma =  1.000   1.189   1.060   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9678  0.0162  0.0160  
## 
## mu =          0.000  -3.036   3.071  
## 
## sigma =  1.000   1.208   1.235   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9678  0.0175  0.0147  
## 
## mu =          0.000   2.656  -2.973  
## 
## sigma =  1.000   1.429   1.227   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9411  0.0302  0.0287  
## 
## mu =          0.000   2.919  -2.955  
## 
## sigma =  1.000   1.151   1.135   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9801  0.0096  0.0103  
## 
## mu =          0.000   3.133  -2.800  
## 
## sigma =  1.000   1.308   1.309   
## 
## noiseSD =    1   
simres2 = basicsim(c(0, 4), c(1))
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6207  0.2055  0.1738  
## 
## mu =          0.000  -3.959   4.111  
## 
## sigma =  1.000   2.590   2.866   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9319  0.0594  0.0087  
## 
## mu =          0.000   1.191  -4.996  
## 
## sigma =  1.000   5.497   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6679  0.1574  0.1747  
## 
## mu =          0.000   4.301  -4.192  
## 
## sigma =  1.000   2.689   2.577   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8613  0.0530  0.0858  
## 
## mu =          0.000   4.752  -4.335  
## 
## sigma =  1.000   1.978   2.190   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5955  0.2104  0.1941  
## 
## mu =          0.000  -3.865   4.376  
## 
## sigma =  1.000   2.444   2.598   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7079  0.1637  0.1284  
## 
## mu =          0.000  -3.962   4.031  
## 
## sigma =  1.000   2.608   2.414   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5424  0.2240  0.2336  
## 
## mu =          0.000  -4.058   4.142  
## 
## sigma =  1.000   2.741   2.618   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7028  0.1502  0.1470  
## 
## mu =          0.000  -4.135   4.212  
## 
## sigma =  1.000   2.778   2.672   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9520  0.0207  0.0272  
## 
## mu =          0.000   4.883  -5.104  
## 
## sigma =  1.000   2.119   1.694   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6278  0.1993  0.1729  
## 
## mu =          0.000  -4.154   4.380  
## 
## sigma =  1.000   2.505   2.513   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8082  0.0934  0.0985  
## 
## mu =          0.000  -4.538   4.220  
## 
## sigma =  1.000   2.400   2.134   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.4921  0.4423  0.0656  
## 
## mu =          0.0000  0.7181 -3.5933 
## 
## sigma =  1.000   4.822   1.114   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5181  0.2587  0.2233  
## 
## mu =          0.000   4.370  -4.362  
## 
## sigma =  1.000   2.932   2.449   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9225  0.0400  0.0375  
## 
## mu =          0.000  -4.254   4.468  
## 
## sigma =  1.000   3.259   2.795   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9619  0.0190  0.0191  
## 
## mu =          0.000   4.931  -4.812  
## 
## sigma =  1.000   2.354   2.549   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9375  0.0347  0.0277  
## 
## mu =          0.000  -4.879   3.824  
## 
## sigma =  1.000   2.150   3.142   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9231  0.0366  0.0402  
## 
## mu =          0.000   4.052  -4.710  
## 
## sigma =  1.000   2.611   1.996   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5765  0.1928  0.2306  
## 
## mu =          0.000   4.223  -3.940  
## 
## sigma =  1.000   2.467   2.838   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9274  0.0220  0.0506  
## 
## mu =          0.000  -4.573   3.128  
## 
## sigma =  1.000   2.151   3.716   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5792  0.2222  0.1986  
## 
## mu =          0.000  -4.159   4.422  
## 
## sigma =  1.000   2.422   2.316   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8495  0.0745  0.0760  
## 
## mu =          0.000  -4.152   4.574  
## 
## sigma =  1.000   2.552   2.246   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9642  0.0187  0.0171  
## 
## mu =          0.000  -2.946   3.674  
## 
## sigma =  1.000   1.163   3.483   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.4555  0.2594  0.2851  
## 
## mu =          0.000  -4.213   4.318  
## 
## sigma =  1.000   2.486   2.430   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5668  0.2060  0.2272  
## 
## mu =          0.000  -4.147   4.239  
## 
## sigma =  1.000   2.436   2.128   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.7873  0.1828  0.0299  
## 
## mu =          0.0000 -0.0123  3.2180 
## 
## sigma =  1.000   4.902   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6732  0.1597  0.1672  
## 
## mu =          0.000  -4.033   4.266  
## 
## sigma =  1.000   2.203   2.565   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9173  0.0356  0.0471  
## 
## mu =          0.000  -4.742   4.116  
## 
## sigma =  1.000   1.976   2.448   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6459  0.1973  0.1568  
## 
## mu =          0.000   4.319  -4.467  
## 
## sigma =  1.000   2.425   2.338   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6592  0.1608  0.1800  
## 
## mu =          0.000  -4.595   4.306  
## 
## sigma =  1.000   2.345   2.213   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6992  0.1251  0.1757  
## 
## mu =          0.000   4.609  -3.753  
## 
## sigma =  1.000   2.391   3.179   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.5.  Rerun with increased df
## Warning: Assuming known noise noiseSD =  1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7722  0.0803  0.1476  
## 
## mu =          0.000   3.956  -4.174  
## 
## sigma =  1.000   2.330   3.073   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7289  0.1214  0.1496  
## 
## mu =          0.000   4.222  -4.525  
## 
## sigma =  1.000   2.368   2.596   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7244  0.1391  0.1365  
## 
## mu =          0.000   3.957  -4.444  
## 
## sigma =  1.000   2.334   2.387   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7615  0.1121  0.1263  
## 
## mu =          0.000  -4.471   3.943  
## 
## sigma =  1.000   2.291   2.256   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5116  0.2439  0.2445  
## 
## mu =          0.000  -4.510   4.342  
## 
## sigma =  1.000   2.414   2.585   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5049  0.2284  0.2667  
## 
## mu =          0.000   4.369  -4.010  
## 
## sigma =  1.000   2.466   2.699   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7099  0.1465  0.1437  
## 
## mu =          0.000  -3.867   4.538  
## 
## sigma =  1.000   2.978   2.491   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5534  0.2338  0.2128  
## 
## mu =          0.000   4.348  -4.186  
## 
## sigma =  1.000   2.240   2.755   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.5548  0.2338  0.2114  
## 
## mu =          0.000  -3.913   4.298  
## 
## sigma =  1.000   2.603   2.665   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7694  0.1188  0.1118  
## 
## mu =          0.000   4.250  -4.373  
## 
## sigma =  1.000   2.252   2.778   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.4463  0.2737  0.2800  
## 
## mu =          0.000   4.080  -4.317  
## 
## sigma =  1.000   2.372   2.134   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8877  0.0546  0.0577  
## 
## mu =          0.000  -4.522   4.240  
## 
## sigma =  1.000   2.237   2.728   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9532  0.0167  0.0302  
## 
## mu =          0.000  -4.571   5.059  
## 
## sigma =  1.000   2.032   1.772   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9719  0.0161  0.0120  
## 
## mu =          0.000  -4.200   4.444  
## 
## sigma =  1.000   4.657   1.911   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9205  0.0427  0.0368  
## 
## mu =          0.000   3.780  -4.569  
## 
## sigma =  1.000   1.755   1.798   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9972  0.0014  0.0014  
## 
## mu =         0.0000  0.4647  0.7202  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.6963  0.1393  0.1644  
## 
## mu =          0.000  -4.049   4.338  
## 
## sigma =  1.000   2.174   2.200   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.4852  0.2711  0.2438  
## 
## mu =          0.000  -4.445   4.496  
## 
## sigma =  1.000   2.403   2.413   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.4957  0.2464  0.2578  
## 
## mu =          0.000  -4.432   4.198  
## 
## sigma =  1.000   2.465   2.414   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9091  0.0208  0.0701  
## 
## mu =          0.0000  3.6510 -0.6115 
## 
## sigma =  1.000   1.234   4.831   
## 
## noiseSD =    1   
simres3 = basicsim(c(0, 4), c(1), bsd = c(rep(1, 500), rep(10, 500)))
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7756  0.0667  0.1577  
## 
## mu =          0.000  -4.211   1.754  
## 
## sigma =  1.000   2.313   4.311   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9589  0.0344  0.0066  
## 
## mu =          0.000   1.359  -4.713  
## 
## sigma =  1.000   5.271   1.496   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8230  0.0901  0.0869  
## 
## mu =          0.000  -4.024   4.284  
## 
## sigma =  1.000   2.187   2.904   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9317  0.0406  0.0277  
## 
## mu =          0.000  -4.144   4.423  
## 
## sigma =  1.000   2.084   2.024   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7654  0.1935  0.0411  
## 
## mu =          0.0000  0.7406 -3.2497 
## 
## sigma =  1.000   4.937   1.216   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE    TRUE   
## 
## pi =     0.8366  0.0353  0.1281  
## 
## mu =          0.0000 -2.7687 -0.1521 
## 
## sigma =  1.000   1.000   4.569   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7642  0.1204  0.1155  
## 
## mu =          0.000   4.127  -3.996  
## 
## sigma =  1.000   2.637   2.600   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8568  0.0751  0.0681  
## 
## mu =          0.000  -3.842   3.989  
## 
## sigma =  1.000   2.684   2.873   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9801  0.0090  0.0109  
## 
## mu =          0.000   5.483  -4.756  
## 
## sigma =  1.000   1.475   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8183  0.0935  0.0882  
## 
## mu =          0.000  -4.187   4.272  
## 
## sigma =  1.000   2.374   2.523   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9126  0.0424  0.0450  
## 
## mu =          0.00   -4.79    4.59   
## 
## sigma =  1.000   2.129   2.119   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7479  0.1239  0.1282  
## 
## mu =          0.000  -3.953   3.862  
## 
## sigma =  1.000   2.776   2.582   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7436  0.1283  0.1281  
## 
## mu =          0.000   4.347  -4.353  
## 
## sigma =  1.000   2.787   2.638   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9568  0.0098  0.0334  
## 
## mu =          0.000   3.213  -1.474  
## 
## sigma =  1.000   1.000   4.777   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9811  0.0110  0.0080  
## 
## mu =          0.000   4.999  -5.216  
## 
## sigma =  1.000   2.365   2.597   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9711  0.0218  0.0071  
## 
## mu =          0.000  -4.695   4.427  
## 
## sigma =  1.000   2.186   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9661  0.0125  0.0213  
## 
## mu =          0.000   4.356  -4.537  
## 
## sigma =  1.000   1.000   1.873   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7680  0.0356  0.1964  
## 
## mu =          0.0000 -4.0084  0.3638 
## 
## sigma =  1.000   1.206   4.900   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE    TRUE   FALSE   
## 
## pi =     0.9668  0.0263  0.0068  
## 
## mu =         0.0000  0.1122  2.7411  
## 
## sigma =  1.000   5.152   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8071  0.0977  0.0952  
## 
## mu =          0.000  -4.388   4.771  
## 
## sigma =  1.000   2.793   2.423   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9220  0.0363  0.0417  
## 
## mu =          0.000  -4.205   4.912  
## 
## sigma =  1.000   2.422   2.348   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9832  0.0133  0.0035  
## 
## mu =          0.000  -2.807   6.261  
## 
## sigma =  1.000   1.304   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7154  0.1450  0.1396  
## 
## mu =          0.00   -4.04    4.25   
## 
## sigma =  1.000   2.615   2.499   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7869  0.0969  0.1162  
## 
## mu =          0.000  -4.166   4.217  
## 
## sigma =  1.000   2.438   2.271   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9012  0.0787  0.0200  
## 
## mu =          0.000   1.762  -3.527  
## 
## sigma =  1.000   5.060   1.337   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8259  0.0285  0.1457  
## 
## mu =          0.0000 -3.8807  0.7694 
## 
## sigma =  1.000   1.000   4.545   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9592  0.0236  0.0171  
## 
## mu =          0.000   4.634  -5.162  
## 
## sigma =  1.000   2.692   1.481   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.7.  Rerun with increased df
## Warning: Assuming known noise noiseSD =  1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8225  0.0827  0.0948  
## 
## mu =          0.000  -4.410   4.341  
## 
## sigma =  1.000   2.608   2.551   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8234  0.0857  0.0909  
## 
## mu =          0.000  -4.530   4.279  
## 
## sigma =  1.000   2.354   2.271   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8254  0.0323  0.1424  
## 
## mu =          0.000   4.365  -1.379  
## 
## sigma =  1.000   1.439   4.965   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8935  0.0370  0.0696  
## 
## mu =          0.000   4.104  -4.466  
## 
## sigma =  1.00    2.61    2.75    
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8683  0.0762  0.0555  
## 
## mu =          0.000  -4.732   4.268  
## 
## sigma =  1.000   2.639   2.334   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8651  0.0647  0.0701  
## 
## mu =          0.000  -4.212   3.893  
## 
## sigma =  1.000   2.590   2.573   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8579  0.1116  0.0305  
## 
## mu =          0.000  -1.305   4.018  
## 
## sigma =  1.000   4.367   1.408   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7535  0.1275  0.1190  
## 
## mu =          0.000  -4.417   4.263  
## 
## sigma =  1.000   2.527   2.549   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7420  0.1267  0.1313  
## 
## mu =          0.000   4.126  -3.902  
## 
## sigma =  1.000   2.596   2.510   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8658  0.0753  0.0589  
## 
## mu =          0.000  -3.369   4.813  
## 
## sigma =  1.000   3.173   2.424   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7840  0.1082  0.1079  
## 
## mu =          0.000  -4.196   4.379  
## 
## sigma =  1.000   2.512   2.001   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7565  0.1242  0.1193  
## 
## mu =          0.000  -3.668   4.151  
## 
## sigma =  1.000   2.930   2.777   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9032  0.0488  0.0480  
## 
## mu =          0.000   4.346  -4.320  
## 
## sigma =  1.000   2.223   2.801   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7223  0.1476  0.1300  
## 
## mu =          0.000  -4.283   4.127  
## 
## sigma =  1.000   2.307   2.488   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9397  0.0294  0.0309  
## 
## mu =          0.00   -4.81    4.62   
## 
## sigma =  1.000   2.454   2.433   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9760  0.0142  0.0098  
## 
## mu =          0.000   5.253  -4.340  
## 
## sigma =  1.000   1.745   1.718   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9859  0.0077  0.0064  
## 
## mu =          0.000   4.593  -4.509  
## 
## sigma =  1.000   2.070   2.731   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.954   0.024   0.022   
## 
## mu =          0.000   3.518  -4.497  
## 
## sigma =  1.000   1.995   1.964   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9972  0.0014  0.0014  
## 
## mu =         0.0000  0.4647  0.7202  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization
## and/or using an empirical null.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.8499  0.0697  0.0804  
## 
## mu =          0.000  -4.045   4.483  
## 
## sigma =  1.000   2.473   2.161   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: f(z) misfit = 1.6.  Rerun with increased df
## Warning: Assuming known noise noiseSD =  1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7497  0.1281  0.1223  
## 
## mu =          0.000  -4.411   4.440  
## 
## sigma =  1.000   2.256   2.502   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model
## Warning: Null proportion pi0 is small. Consider increasing penalization and/or using an empirical null.
## Warning: Using an empirical null with a fitted noiseSD gives a substantially different model. Consider rerunning with theonull = FALSE and noiseSD = NA.
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.7265  0.1393  0.1342  
## 
## mu =          0.000  -3.966   4.097  
## 
## sigma =  1.000   2.605   2.483   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9701  0.0136  0.0163  
## 
## mu =          0.000  -2.413   4.441  
## 
## sigma =  1.000   5.017   2.006   
## 
## noiseSD =    1   
simres4 = basicsim(c(0, 4), c(1), bsd = c(rep(1, 500), rep(10, 500)), minpi0 = 0.9, 
    seed = 200)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9949  0.0017  0.0034  
## 
## mu =          0.000   6.049  -3.340  
## 
## sigma =  1.000   2.870   1.139   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9861  0.0079  0.0060  
## 
## mu =          0.000  -3.623   3.924  
## 
## sigma =  1.000   1.381   1.854   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9831  0.0102  0.0066  
## 
## mu =          0.000  -5.444   5.692  
## 
## sigma =  1.000   2.764   2.315   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9958  0.0018  0.0023  
## 
## mu =          0.000  -6.904   2.326  
## 
## sigma =  1.000   1.218   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9947  0.0031  0.0022  
## 
## mu =          0.000  -3.470   4.144  
## 
## sigma =  1.000   1.227   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9851  0.0088  0.0061  
## 
## mu =          0.000  -6.976   5.662  
## 
## sigma =  1.000   1.610   1.072   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9900  0.0036  0.0064  
## 
## mu =          0.000  -6.310   4.703  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9930  0.0029  0.0041  
## 
## mu =          0.000   5.316  -5.070  
## 
## sigma =  1.000   1.000   1.142   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9840  0.0109  0.0051  
## 
## mu =          0.000   4.392  -6.218  
## 
## sigma =  1.000   2.772   2.526   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9781  0.0089  0.0130  
## 
## mu =          0.000  -4.965   5.178  
## 
## sigma =  1.000   2.943   1.922   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9885  0.0048  0.0067  
## 
## mu =          0.000  -3.797   5.908  
## 
## sigma =  1.000   1.000   2.229   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9961  0.0020  0.0019  
## 
## mu =          0.000  -6.604   7.509  
## 
## sigma =  1.000   2.481   2.715   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9922  0.0046  0.0032  
## 
## mu =          0.000   4.372  -4.488  
## 
## sigma =  1.000   1.000   1.008   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9858  0.0092  0.0050  
## 
## mu =          0.000   5.094  -5.443  
## 
## sigma =  1.000   3.066   1.482   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9902  0.0050  0.0048  
## 
## mu =          0.000   3.697  -3.940  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9796  0.0111  0.0093  
## 
## mu =          0.000   4.144  -5.375  
## 
## sigma =  1.000   1.772   1.248   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9958  0.0032  0.0010  
## 
## mu =          0.000  -6.060   7.138  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9880  0.0048  0.0072  
## 
## mu =          0.000   5.693  -4.072  
## 
## sigma =  1.000   2.031   2.255   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9940  0.0038  0.0023  
## 
## mu =         0.000   4.000   3.818   
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: glm.fit: fitted rates numerically 0 occurred
## Warning: Assuming known noise noiseSD =  1 . If needed rerun with noiseSD = NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9808  0.0067  0.0126  
## 
## mu =          0.000  -5.838   2.627  
## 
## sigma =  1.000   1.690   3.831   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9777  0.0039  0.0183  
## 
## mu =          0.000  -3.710   2.572  
## 
## sigma =  1.000   1.000   4.878   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9875  0.0048  0.0077  
## 
## mu =          0.000  -4.251   5.391  
## 
## sigma =  1.000   1.212   2.243   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9854  0.0042  0.0104  
## 
## mu =          0.000   4.955  -3.597  
## 
## sigma =  1.000   3.219   1.266   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9904  0.0041  0.0055  
## 
## mu =          0.000  -5.437   5.722  
## 
## sigma =  1.000   2.949   1.427   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9882  0.0034  0.0084  
## 
## mu =          0.000  -3.492   6.949  
## 
## sigma =  1.000   1.627   2.829   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9725  0.0089  0.0186  
## 
## mu =          0.000   5.931  -4.074  
## 
## sigma =  1.000   2.356   1.715   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9900  0.0023  0.0077  
## 
## mu =          0.000  -4.905   4.583  
## 
## sigma =  1.000   1.000   1.246   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9913  0.0060  0.0026  
## 
## mu =          0.0000  0.6215 -3.4060 
## 
## sigma =  1.000   6.163   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE    TRUE   
## 
## pi =     0.9970  0.0016  0.0014  
## 
## mu =          0.0000  0.7701 -0.0227 
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## Warning: longer object length is not a multiple of shorter object length
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9959  0.0023  0.0018  
## 
## mu =          0.000  -5.319   6.127  
## 
## sigma =  1.000   2.772   1.000   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9820  0.0075  0.0105  
## 
## mu =          0.000   5.854  -3.265  
## 
## sigma =  1.000   1.454   1.483   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9873  0.0047  0.0079  
## 
## mu =          0.000  -7.166  -3.745  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: Assuming known noise noiseSD = 1 . If needed rerun with noiseSD =
## NA to fit noiseSD.
## Fitting preliminary model 
## Fitting final model 
## 
## Fitted Model: J = 3 groups
## ----------------------------
## null?         TRUE   FALSE   FALSE   
## 
## pi =     0.9969  0.0019  0.0012  
## 
## mu =          0.000   4.961  -2.635  
## 
## sigma =  1   1   1   
## 
## noiseSD =    1   
## 
## 
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted rates numerically 0 occurred
## Error: system is computationally singular: reciprocal condition number =
## 1.78038e-17

Show illustrative example

altcol = "cyan"  #colors to use
nullcol = "blue"
nc = 40  #number of bins in histograms
ncz = 100  # number of bins in z score histograms


plot_FDR_hist = function(sim, iter = 1) {
    hh.pval = sim$pval[[iter]]
    hh.zscore = sim$zscore[[iter]]
    hh.hist = hist(hh.pval, freq = FALSE, xlab = "p value", main = "Distribution of p values", 
        nclass = nc, col = altcol)

    hh.q = qvalue(hh.pval)
    abline(h = hh.q$pi0, col = nullcol, lwd = 2)

    hh.hist$density = rep(hh.q$pi0, length(hh.hist$density))
    plot(hh.hist, add = TRUE, col = nullcol, freq = FALSE)

    abline(v = 0.1, lwd = 2, col = 2)

    text(0.05, 1.2, labels = "A", col = 2, cex = 1.2)
    text(0.05, 0.4, labels = "B", col = 2, cex = 1.2)
    text(0.6, 3, labels = paste0("FDR = B/(A+B) =  ", round(hh.q$pi0 * 0.1 * 
        length(hh.pval)/sum(hh.pval < 0.1), 2)), cex = 1.2)
}
plot_FDR_hist(simres1, 1)

plot of chunk unnamed-chunk-3

plot_lfdr_hist = function(sim, iter = 1) {
    require(fdrtool)
    hh.pval = sim$pval[[iter]]
    hh.hist = hist(hh.pval, freq = FALSE, xlab = "p value", main = "Distribution of p values", 
        nclass = nc, col = altcol)

    hh.gren = grenander(ecdf(hh.pval))
    abline(h = min(hh.gren$f.knots), col = nullcol, lwd = 2)
    lines(hh.gren$x.knots, hh.gren$f.knots, lwd = 2)
    abline(v = 0.1, lwd = 2, col = 2)
    text(0.1, 0.9, labels = "a", col = 2, cex = 1)
    text(0.1, 0.34, labels = "b", col = 2, cex = 1.2)
    text(0.6, 3, labels = paste0("lfdr = b/(a+b) =  ", round(min(hh.gren$f.knots)/approx(hh.gren$x.knots, 
        hh.gren$f.knots, 0.1)$y, 2)), cex = 1.2)
}
plot_lfdr_hist(simres1, 1)

plot of chunk unnamed-chunk-4

# plot a histogram of z scores, highlighting the alternative distribution of
# z scores that is implied by localfdr values lfdr.
nullalthist = function(z, lfdr, ...) {
    h = hist(z, freq = FALSE, col = nullcol, nclass = ncz, ...)
    avlfdr = unlist(lapply(split(lfdr, cut(z, h$breaks), drop = FALSE), mean))
    h$density = (1 - avlfdr) * h$density
    plot(h, add = TRUE, col = altcol, freq = FALSE)
}

# this one puts the null on the bottom
altnullhist = function(z, lfdr, ...) {
    h = hist(z, freq = FALSE, col = altcol, nclass = ncz, ...)
    avlfdr = unlist(lapply(split(lfdr, cut(z, h$breaks), drop = FALSE), mean))
    h$density = avlfdr * h$density
    plot(h, add = TRUE, col = nullcol, freq = FALSE)
}

plotall_hist = function(sim, iter = 1, histfun = nullalthist) {
    hh.zscore = sim$zscore[[iter]]
    par(mfcol = c(2, 2))
    histfun(hh.zscore, sim$betahat.fdrtool[[iter]]$lfdr, main = "fdrtool")
    histfun(hh.zscore, sim$betahat.locfdr[[iter]]$fdr, main = "locfdr")
    histfun(hh.zscore, sim$betahat.mixfdr[[iter]]$fdr, main = "mixfdr")
    histfun(hh.zscore, sim$betahat.ash.n[[iter]]$lfdr, main = "ash")
    par(mfcol = c(1, 1))
}

# pdf('figures/nullalthist.pdf')
plotall_hist(simres1, 1, nullalthist)

plot of chunk unnamed-chunk-5

# dev.off()

# pdf('figures/altnullhist.pdf')
plotall_hist(simres1, 1, altnullhist)

plot of chunk unnamed-chunk-5

# dev.off()
# par(mfcol=c(3,3))
plot_ecdf = function(sims) {
    for (i in 1:length(sims$beta)) {
        plot(ecdf(sims$beta[[i]]), xlim = c(-6, 6), main = paste0("iteration ", 
            i))
        x = seq(-6, 6, length = 1000)
        lines(cdf.ash(sims$betahat.ash.n[[i]], x), col = 2, lwd = 2)
        lines(cdf.ash(sims$betahat.ash.u[[i]], x), col = 3, lwd = 2)
        lines(cdf.ash(sims$betahat.ash.true[[i]], x), col = 4, lwd = 2)
    }
}
plot_ecdf(simres1)

plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6

plot_ecdf(simres2)

plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6 plot of chunk unnamed-chunk-6

# Plot pi0 from each method
get_pi0.fdrtool = function(f) {
    f$param[3]
}
get_pi0.locfdr = function(f) {
    f$fp0[1, 3]
}
get_pi0.mixfdr = function(f) {
    f$pi[1]
}

plot_pi0 = function(sims) {
    pi0 = sims$pi0
    pi0_ash.n = unlist(lapply(sims$betahat.ash.n, get_pi0))
    pi0_ash.u = unlist(lapply(sims$betahat.ash.u, get_pi0))
    pi0_fdrtool = unlist(lapply(sims$betahat.fdrtool, get_pi0.fdrtool))
    pi0_locfdr = unlist(lapply(sims$betahat.locfdr, get_pi0.locfdr))
    pi0_mixfdr = unlist(lapply(sims$betahat.mixfdr, get_pi0.mixfdr))
    pi0_qval = unlist(lapply(sims$betahat.qval, "[[", "pi0"))

    res = data.frame(pi0 = pi0, qvalue = pi0_qval, mixfdr = pi0_mixfdr, locfdr = pi0_locfdr, 
        fdrtool = pi0_fdrtool, ash.nullbiased = pi0_ash.n, ash.uniform = pi0_ash.u)
    require(reshape2)
    res.melt = melt(res, id.vars = c("pi0"), variable.name = "Method")
    p = ggplot(data = res.melt, aes(pi0, value, colour = Method)) + geom_point(shape = 16) + 
        geom_abline(colour = "black") + xlab("True pi0") + ylab("Estimated pi0")
    print(p + scale_y_continuous(limits = c(0, 1)) + scale_x_continuous(limits = c(0, 
        1)) + coord_equal(ratio = 1))

}

plot_pi1 = function(sims) {
    pi0 = sims$pi0
    pi0_ash.n = unlist(lapply(sims$betahat.ash.n, get_pi0))
    pi0_ash.u = unlist(lapply(sims$betahat.ash.u, get_pi0))
    pi0_fdrtool = unlist(lapply(sims$betahat.fdrtool, get_pi0.fdrtool))
    pi0_locfdr = unlist(lapply(sims$betahat.locfdr, get_pi0.locfdr))
    pi0_mixfdr = unlist(lapply(sims$betahat.mixfdr, get_pi0.mixfdr))
    pi0_qval = unlist(lapply(sims$betahat.qval, "[[", "pi0"))

    res = data.frame(pi0 = pi0, qvalue = pi0_qval, mixfdr = pi0_mixfdr, locfdr = pi0_locfdr, 
        fdrtool = pi0_fdrtool, ash.nullbiased = pi0_ash.n, ash.uniform = pi0_ash.u)
    require(reshape2)
    res.melt = melt(res, id.vars = c("pi0"), variable.name = "Method")
    p = ggplot(data = res.melt, aes(1 - pi0, log2((1 - value)/(1 - pi0)), colour = Method)) + 
        geom_point(shape = 16) + # geom_abline(colour = 'black') +
    xlab("True pi1") + ylab("log2(Estimated pi1/True pi1)")
    print(p + scale_y_continuous(limits = c(-4, 4)) + scale_x_continuous(limits = c(0, 
        1)))

}

pdf("figures/estpi0_sim1.pdf")
plot_pi0(simres1)
## Loading required package: reshape2
## Warning: Removed 13 rows containing missing values (geom_point).
dev.off()
## pdf 
##   2
pdf("figures/estpi0_sim2.pdf")
plot_pi0(simres2)
dev.off()
## pdf 
##   2

Figure to show that estimated betahats are not so different

plot(betahat.ash.u[[1]]$PosteriorMean, betahat.ash.n[[1]]$PosteriorMean)
## Error: object 'betahat.ash.u' not found
abline(a = 0, b = 1, lwd = 2, col = 2)
## Error: plot.new has not been called yet
plot(betahat.ash.u[[1]]$PosteriorSD, betahat.ash.n[[1]]$PosteriorSD)
## Error: object 'betahat.ash.u' not found
abline(a = 0, b = 1, lwd = 2, col = 2)
## Error: plot.new has not been called yet
rmse = function(x, y) {
    sqrt(mean((x - y)^2))
}
get_rmse.ash = function(a, b) {
    rmse(a$PosteriorMean, b)
}
get_rmse.mixfdr = function(a, b) {
    rmse(a$effectSize, b)
}
plot_rmse = function(sims, inczero = FALSE, incbetahat = FALSE) {
    err.ash.n = mapply(get_rmse.ash, sims$betahat.ash.n, sims$beta)
    err.ash.u = mapply(get_rmse.ash, sims$betahat.ash.u, sims$beta)
    err.mixfdr = mapply(get_rmse.mixfdr, sims$betahat.mixfdr, sims$beta)
    err.betahat = mapply(rmse, sims$betahat, sims$beta)
    err.zero = unlist(lapply(sims$beta, rmse, y = 0))

    res = data.frame(mixfdr = err.mixfdr, ash.nullbiased = err.ash.n, ash.uniform = err.ash.u)
    if (inczero) {
        res = data.frame(res, zero = err.zero)
    }
    if (incbetahat) {
        res = data.frame(res, betahat = err.betahat)
    }
    require(reshape2)
    res.melt = melt(res, id.vars = c("ash.uniform"), variable.name = "Method")

    p = ggplot(data = res.melt, aes(ash.uniform, value, colour = Method)) + 
        geom_point(shape = 16) + geom_abline(colour = "black") + xlab("RMSE (ash.uniform)") + 
        ylab("RMSE (other method)")
    print(p + scale_y_continuous(limits = c(0, max(res))) + scale_x_continuous(limits = c(0, 
        max(res))) + coord_equal(ratio = 1))
}
pdf("figures/rmse_sim1.pdf")
plot_rmse(simres1)
dev.off()
## pdf 
##   2
pdf("figures/rmse_sim2.pdf")
plot_rmse(simres2)
dev.off()
## pdf 
##   2
plot_LR = function(sims) {
    hist(unlist(lapply(sims$betahat.ash.u, get_loglik)) - unlist(lapply(sims$betahat.ash.n, 
        get_loglik)), xlab = "loglik difference", main = "loglik differences for nullbiased prior vs mle", 
        nclass = 10)
}

pdf("figures/logLR.pdf")
plot_LR(simres1)
plot_LR(simres2)
dev.off()
## pdf 
##   2
mean_quant = function(x, mult = 1) {
    x <- na.omit(x)
    sd <- mult * sqrt(var(x))
    mean <- mean(x)
    data.frame(y = median(x), ymin = quantile(x, 0.25), ymax = quantile(x, 0.75))
}

# ptype indicates what type of plot to do maxlfsr controls maximum x axis
# value maxy controls maximum y axis value
plot_lfsr = function(sims, maxlfsr = 0.1, ptype = c("lfsr", "lfsra", "lfdr"), 
    maxy = 1) {
    ptype = match.arg(ptype)
    xlabtype = ifelse(ptype == "lfdr", "lfdr", "lfsr")
    res = list()
    for (i in 1:length(sims)) {
        lfsr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", ptype))
        lfsr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", ptype))
        if (ptype == "lfdr") {
            lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", 
                "lfdr"))
            lfdr.mixfdr = unlist(lapply(sims[[i]]$betahat.mixfdr, "[[", "fdr"))
            lfdr.locfdr = unlist(lapply(sims[[i]]$betahat.locfdr, "[[", "fdr"))
        } else {
            lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", 
                "lfsr"))
        }

        subset = lfsr.ash.true < maxlfsr

        if (length(subset) > 0) {
            res[[i]] = data.frame(Scenario = i, ash.nullbiased = lfsr.ash.n[subset], 
                ash.uniform = lfsr.ash.u[subset], Bayes = 0.1 * maxlfsr * findInterval(lfsr.ash.true[subset], 
                  seq(0, maxlfsr, length = 11)) - 0.05 * maxlfsr)
            if (ptype == "lfdr") {
                res[[i]] = data.frame(res[[i]], mixfdr = lfdr.mixfdr[subset])
            }
        }
    }

    require(reshape2)

    res.melt = melt(res, id.vars = c("Bayes", "Scenario"), variable.name = "Method")


    cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", 
        "#D55E00", "#CC79A7")
    labels = c("ash (null-biased)", "ash (uniform)")
    breaks = c("ash.nullbiased", "ash.uniform")
    if (ptype == "lfdr") {
        labels = c("mixfdr", labels)
        breaks = c("mixfdr", breaks)
    }

    p = ggplot(data = res.melt, aes(Bayes, value, colour = Method)) + facet_grid(. ~ 
        Scenario) + # scale_fill_manual(values=cbbPalette) +
    # scale_colour_manual(values=cbbPalette) +
    geom_point(size = 1, alpha = 0.1) + stat_smooth(se = FALSE, size = 2) + 
        stat_summary(fun.data = "mean_quant", geom = "ribbon", alpha = 0.25) + 
        geom_abline(colour = "black") + xlab(paste0(xlabtype, " (Bayes)")) + 
        ylab(paste0(ptype, " (Method)"))

    print(p + scale_y_continuous(limits = c(0, maxy)) + scale_x_continuous(limits = c(0, 
        maxlfsr)) + scale_colour_manual(values = cbbPalette, breaks = breaks, 
        labels = labels))
}
# plots estimated lfdr and lfsr against truth. ptype indicates whether to
# use lfsr or lfsra maxx controls maximum x axis value maxy controls maximum
# y axis value
plot_lfsdr = function(sims, maxx = 0.1, ptype = c("lfsr", "lfsra"), maxy = 1) {
    ptype = match.arg(ptype)
    res = list()
    res.lfsr = list()
    res.lfdr = list()

    for (i in 1:length(sims)) {
        lfsr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", "lfsr"))
        lfsr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", ptype))
        lfsr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", ptype))

        lfdr.ash.n = unlist(lapply(sims[[i]]$betahat.ash.n, "[[", "lfdr"))
        lfdr.ash.u = unlist(lapply(sims[[i]]$betahat.ash.u, "[[", "lfdr"))
        lfdr.ash.true = unlist(lapply(sims[[i]]$betahat.ash.true, "[[", "lfdr"))
        lfdr.mixfdr = unlist(lapply(sims[[i]]$betahat.mixfdr, "[[", "fdr"))
        lfdr.locfdr = unlist(lapply(sims[[i]]$betahat.locfdr, "[[", "fdr"))


        subset = lfsr.ash.true < maxx

        res.lfsr[[i]] = data.frame(Scenario = i, Measure = "lfsr", ash.nullbiased = lfsr.ash.n[subset], 
            ash.uniform = lfsr.ash.u[subset], Bayes = 0.1 * maxx * findInterval(lfsr.ash.true[subset], 
                seq(0, maxx, length = 11)) - 0.05 * maxx, mixfdr = NA)

        subset = lfdr.ash.true < maxx
        res.lfdr[[i]] = data.frame(Scenario = i, Measure = "lfdr", ash.nullbiased = lfdr.ash.n[subset], 
            ash.uniform = lfdr.ash.u[subset], Bayes = 0.1 * maxx * findInterval(lfdr.ash.true[subset], 
                seq(0, maxx, length = 11)) - 0.05 * maxx, mixfdr = lfdr.mixfdr[subset])


        res[[i]] = rbind(res.lfdr[[i]], res.lfsr[[i]])
    }

    require(reshape2)

    res.melt = melt(res, id.vars = c("Bayes", "Scenario", "Measure"), variable.name = "Method")

    cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", 
        "#D55E00", "#CC79A7")
    labels = c("mixfdr", "ash (null-biased)", "ash (uniform)")
    breaks = c("mixfdr", "ash.nullbiased", "ash.uniform")

    p = ggplot(data = res.melt, aes(Bayes, value, colour = Method)) + facet_grid(Measure ~ 
        Scenario) + # scale_fill_manual(values=cbbPalette) +
    # scale_colour_manual(values=cbbPalette) +
    geom_point(size = 1, alpha = 0.1) + # stat_smooth(se=FALSE,size=2) +
    stat_summary(fun.data = "mean_quant", geom = "ribbon", alpha = 0.25) + geom_abline(colour = "red", 
        size = 1) + xlab("Truth") + ylab("Estimate")

    print(p + scale_y_continuous(limits = c(0, maxy)) + scale_x_continuous(limits = c(0, 
        maxx)) + scale_colour_manual(values = cbbPalette, breaks = breaks, labels = labels))
}
png("figures/lfsdr_sim1sim2_blowup.png", height = 427, width = 720)
plot_lfsdr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsra")
## Warning: Removed 2 rows containing missing values (stat_summary).
## Warning: Removed 4542 rows containing missing values (stat_summary).
## Warning: Removed 50588 rows containing missing values (stat_summary).
## Warning: Removed 14167 rows containing missing values (stat_summary).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 4542 rows containing missing values (geom_point).
## Warning: Removed 50588 rows containing missing values (geom_point).
## Warning: Removed 14167 rows containing missing values (geom_point).
dev.off()
## pdf 
##   2
png("figures/lfdr_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfdr")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 2 rows containing missing values (stat_smooth).
## Warning: Removed 2 rows containing missing values (stat_summary).
## Warning: Removed 2 rows containing missing values (geom_point).
dev.off()
## pdf 
##   2
png("figures/lfsra_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsra")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 1 rows containing missing values (stat_smooth).
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 1 rows containing missing values (stat_smooth).
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 292 rows containing missing values (stat_smooth).
## Warning: Removed 307 rows containing missing values (stat_smooth).
## Warning: Removed 1 rows containing missing values (stat_summary).
## Warning: Removed 1 rows containing missing values (stat_summary).
## Warning: Removed 599 rows containing missing values (stat_summary).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 599 rows containing missing values (geom_point).
dev.off()
## pdf 
##   2
png("figures/lfsra_sim1sim2_blowup.png", height = 160, width = 540)
plot_lfsr(list(simres1, simres1a, simres2), 0.1, ptype = "lfsr")
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## Warning: Removed 9 rows containing missing values (stat_smooth).
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 16 rows containing missing values (stat_summary).
## Warning: Removed 16 rows containing missing values (geom_point).
dev.off()
## pdf 
##   2
plot_pi0(simres3)
## Warning: Removed 1 rows containing missing values (geom_point).

plot of chunk unnamed-chunk-17

plot_pi1(simres3)
## Warning: NaNs produced
## Warning: Removed 5 rows containing missing values (geom_point).

plot of chunk unnamed-chunk-17

QUestion: is the null-biased prior maybe a little too conservative? Answer: log likelihoods don't suggest they are

# hh.ashtrue = hh.ashz hh.ashtrue$fitted.g$pi =
# c(2/3,1/15,1/15,1/15,1/15,1/15) hh.ashtrue$fitted.g$mean = c(0,0,0,0,0,0)
# hh.ashtrue$fitted.g$sd = sqrt(c(0,1,0.2,0.4,0.8,3))

# loglik(hh.ashtrue,betahat,sebetahat) loglik(hh.ashz,betahat,sebetahat)